A Deep Learning Based Ensemble Model for Generalized Mitosis Detection in H &E Stained Whole Slide Images

Abstract

Identification of mitotic cells as well as estimation of mitotic index are important parameters in understanding the pathology of cancer, predicting response to chemotherapy and overall survival. This is usually performed manually by pathologists and there can be considerable variability in their assessments. The use of deep learning(DL) models can help in addressing this issue. However, most of the state-of-the-art methods are trained for specific cancer types, and often tend to fail when used across multiple tumor types. Hence there is a clear need for a more ‘pan-tumor’ approach to identifying mitotic figures. We propose a generalized DL model for mitosis detection using the MIDOG-2022 Challenge dataset. Using an ensemble of predictions from a transformer-based object detector and a separate classifier, our model makes final predictions. Our approach achieved an F1-score of 0.7569 and stood second in the MIDOG-2022 challenge. The predictions from the object detector alone achieved an F1-score of 0.7510. Our model generalizes well to address the domain shifts caused by variability in image acquisition, protocols and tumor tissue types.

S. Kotte and V.G. Saipradeep—Joint first author.